The dataset that I am using for the task of IPL 2022 analysis is downloaded from Kaggle.
Every sporting event today generates a lot of data about the game, which is used to analyze the performance of players, teams, and every event of the game. So the use of data science is in every sport today.
In this report, we delve into the statistics, trends, and insights emerging from IPL 2022, offering a comprehensive overview of the tournament's performance and player contributions. Leveraging statistical analysis and visualization, our analysis aims to uncover key match attributes and performances in IPL 2022 . This report enriches the discourse around IPL 2022, providing stakeholders, enthusiasts, and cricket aficionados with a data-driven perspective on the tournament's triumphs, challenges, and memorable moments.
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv("Book_ipl22_ver_33.csv")
print(data.head())
match_id date venue \
0 1 March 26,2022 Wankhede Stadium, Mumbai
1 2 March 27,2022 Brabourne Stadium, Mumbai
2 3 March 27,2022 Dr DY Patil Sports Academy, Mumbai
3 4 March 28,2022 Wankhede Stadium, Mumbai
4 5 March 29,2022 Maharashtra Cricket Association Stadium,Pune
team1 team2 stage toss_winner toss_decision first_ings_score \
0 Chennai Kolkata Group Kolkata Field 131
1 Delhi Mumbai Group Delhi Field 177
2 Banglore Punjab Group Punjab Field 205
3 Gujarat Lucknow Group Gujarat Field 158
4 Hyderabad Rajasthan Group Hyderabad Field 210
first_ings_wkts second_ings_score second_ings_wkts match_winner won_by \
0 5 133 4 Kolkata Wickets
1 5 179 6 Delhi Wickets
2 2 208 5 Punjab Wickets
3 6 161 5 Gujarat Wickets
4 6 149 7 Rajasthan Runs
margin player_of_the_match top_scorer highscore best_bowling \
0 6 Umesh Yadav MS Dhoni 50 Dwayne Bravo
1 4 Kuldeep Yadav Ishan Kishan 81 Kuldeep Yadav
2 5 Odean Smith Faf du Plessis 88 Mohammed Siraj
3 5 Mohammed Shami Deepak Hooda 55 Mohammed Shami
4 61 Sanju Samson Aiden Markram 57 Yuzvendra Chahal
best_bowling_figure
0 3--20
1 3--18
2 2--59
3 3--25
4 3--22
The dataset contains all the information needed to summarize the story of IPL 2022 so far. So let’s start by looking at the number of matches won by each team in IPL 2022:
figure = px.histogram(data, x=data["match_winner"],
title="Number of Matches Won in IPL 2022")
figure.show()
Now let’s see how most of the teams win. Here we will analyze whether most of the teams win by defending (batting first) or chasing (batting second):
data["won_by"] = data["won_by"].map({"Wickets": "Chasing",
"Runs": "Defending"})
won_by = data["won_by"].value_counts()
label = won_by.index
counts = won_by.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Number of Matches Won By Defending Or Chasing')
fig.update_traces(hoverinfo='label+percent', textinfo='value',
textfont_size=30,
marker=dict(colors=colors,
line=dict(color='black', width=3)))
fig.show()
Now let’s see what most teams prefer (batting or fielding) after winning the toss:
toss = data["toss_decision"].value_counts()
label = toss.index
counts = toss.values
colors = ['skyblue','yellow']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Toss Decision')
fig.update_traces(hoverinfo='label+percent',
textinfo='value', textfont_size=30,
marker=dict(colors=colors,
line=dict(color='black', width=3)))
fig.show()
Now let’s see the top scorers of most IPL 2022 matches:
figure = px.bar(data, x=data["top_scorer"],
title="Top Scorers in IPL 2022")
figure.show()
Let’s analyze it deeply by including the runs scored by the top scorers:
figure = px.bar(data, x=data["top_scorer"],
y = data["highscore"],
color = data["highscore"],
title="Top Scorers in IPL 2022")
figure.show()
Now let’s have a look at the most player of the match awards till now in IPL 2022:
figure = px.bar(data, x = data["player_of_the_match"],
title="Most Player of the Match Awards")
figure.show()
Now let’s have a look at the bowlers with the best bowling figures in most of the matches:
figure = px.bar(data, x=data["best_bowling"],
title="Best Bowlers in IPL 2022")
figure.show()
Now let’s have a look at whether most of the wickets fall while setting the target or while chasing the target:
figure = go.Figure()
figure.add_trace(go.Bar(
x=data["venue"],
y=data["first_ings_wkts"],
name='First Innings Wickets',
marker_color='gold'
))
figure.add_trace(go.Bar(
x=data["venue"],
y=data["second_ings_wkts"],
name='Second Innings Wickets',
marker_color='lightgreen'
))
figure.update_layout(barmode='group', xaxis_tickangle=-45)
figure.show()
So this is how you can perform the task of IPL 2022 analysis using Python. IPL 2022 is going great for Gujrat as a new team this year. Jos Buttler and KL Rahul have been great with the bat, and Yuzvendra Chahal and Kuldeep Yadav have been great with the bowl. I hope you liked this article on IPL 2022 analysis using Python. Feel free to ask valuable questions in the comments section below.